ANALISIS FORENSIK MOBILE-TROJAN METASPLOIT DENGAN MACHINE LEARNING (DECISION TREE)

AMAL, MUHAMMAD IKHLASUL and Stiawan, Deris (2024) ANALISIS FORENSIK MOBILE-TROJAN METASPLOIT DENGAN MACHINE LEARNING (DECISION TREE). Undergraduate thesis, Sriwijaya University.

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Abstract

The rapid development of mobile technology has opened the door to new advantages in terms of connectivity and productivity. However, its impact on security is becoming increasingly significant, especially with regard to malware attacks on mobile devices. This threat brings serious risks to the confidentiality and integrity of user data. In this research, the malware that will be the object of research is trojan malware, which is a type of malware that presents itself as a benign or harmless application, to attract victims to download and install malware. Trojans store system information within themselves, or open infected computers to remote access and transmit information to other computers via the internet. Therefore, this research aims to face these challenges through a forensic analysis approach, using machine learning methods, especially the decision tree algorithm in performing detection and visualization and applying the digital forensic investigation process procedure from the National Institute of Standards and Technology (NIST) as a research flow from the initial stage, namely data collection to the final stage, namely reporting. The research that has been carried out has succeeded in obtaining the best accuracy results using the decision tree method in the validation ratio of 40% training data comparison and 60% testing data with accuracy performance of 99.87%, recall value 97.03%, precision value 98.44%, and F1-Score value 97.73% and specificity value 98.37%. and obtained a visualization score of 100% testing score and 99.91% validation score.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Malware, Trojan, Decision Tree, forensik, deteksi, visualisasi.
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Ikhlasul Amal
Date Deposited:	10 Jul 2024 06:04
Last Modified:	10 Jul 2024 06:04
URI:	http://repository.unsri.ac.id/id/eprint/150032

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